Simulated Library Research Articles

A novel meta‐learning network for partial discharge source localization in gas‐insulated switchgear via digital twin

AbstractDue to the requirement for highly precise synchronous sampling and the substantial reliance on time difference calculations, the current partial discharge (PD) localization based on the time difference of arrival is only applicable in certain situations. As digital twin technology has advanced, it is possible to employ virtual models to support gas‐insulated switchgear (GIS) PD localization. To do this, we propose a meta‐learning (ML) network with the aid of digital twin for actual GIS PD localization. Firstly, a GIS digital twin model was established to acquire an auxiliary simulated sample library. Then, a temporal convolutional network is established to extract the discriminable features, effectively obtain the time dependence between features, and improve the accuracy of localization. Next, ML is adopted to quickly learn meta‐knowledge that can be applied across tasks, and the model's sensitivity to task changes is improved. Finally, the model is fine‐tuned through a limited number of samples from the target task, and high precise PD localization is achieved. The experimental results demonstrate that the ML has an average localization error of only 9.25 cm and a probability density rose to 93% within 20 cm, which is clearly superior to previous methods.

Data independent acquisition for gas chromatographic MS/MS analysis of volatile compounds

This study presents a novel tandem mass spectrometry (MS/MS) approach utilizing a data independent acquisition (DIA) concept specifically designed with gas chromatography−electron ionization−triple quadrupole mass spectrometry (GC−EI-QqQMS). This allows compound identification based on comparison between all the experimental MS/MS product ion spectra and the simulated library data of >1,000 MS/MS transitions of 71 compounds. The simulation data were generated by using the Competitive Fragmentation Modeling (CFM-ID) 3.0 program. The approach for calculation of the DIA MS/MS library match scores was then established and applied for identification of a range of terpenoids and oxygenated compounds in perfume. The identity of each peak was confirmed using 4–241 MS/MS transitions. The established data collection and analysis methods are expected to be useful for increased confidence in volatile compound analysis.

KNN Based Denoising Algorithm for Photon-Counting LiDAR: Numerical Simulation and Parameter Optimization Design

Photon-counting LiDAR can obtain long-distance, high-precision target3D geographic information, but extracting high-precision signal photons from background noise photons is the key premise of photon-counting LiDAR data processing and application. This study proposes an adaptive noise filtering algorithm that adjusts parameters according to the background photon count rate and removes noise photons based on the local mean Euclidean distance. A simulated photon library that provides different background photon count rates and detection probabilities was constructed. It was then used to fit the distribution relationship between the background photon count rate and the average KNN (K-Nearest Neighbor) distance (k = 2–6) and to obtain the optimal denoising threshold under different background photon count rates. Finally, the proposed method was evaluated by comparing it with the modified density-based spatial clustering (mDBSCAN) and local distance-based statistical methods. The experimental results show that various methods are similar when the background noise rate is high. However, at most non-extreme background photon count rate levels, the F of this algorithm was maintained between 0.97–0.99, which is an improvement over other classical algorithms. The new strategy eliminated the artificial introduction of errors. Due to its low error rates, the proposed method can be widely applied in photon-counting LiDAR signal extraction under various conditions.

Determining metamorphic grade from greenschist to amphibolite using Hyperion data: the case of an inverted metamorphic gradient of Almora nappe (lesser Himalaya)

The intricate nature of metamorphic terrain remains a complex problem for many remote sensing scientists. The metamorphic rock assemblages are composed by intrinsic mixtures of index minerals which are defined at different pressure and temperature zones. Metamorphic assemblage’s nature and geothermal gradient can be identified using reflectance spectroscopy. The study aims to determine the nature and gradient of metamorphism using spectral identification of the index mineral phases. A model for metamorphic assemblage was developed to predict the nature and grade of metamorphism in order to address the problem with the inadequate quantification of mineral spectral mixing. The study addresses the problem by incorporating semi-empirical method of classification of metamorphic rock assemblage based on reflectance characteristics. We use the simulated spectral library of Hyperion satellite data which is favourable for classifying geological outcrops in metamorphic terrain of the Almora nappe of lower Himalayas. The Hyperion data analysis reveals the nature and trend of change of the metamorphic grades within different zones based on mass proportion of index mineral assemblages. An inverse thermal grade of the metamorphic Almora group is observed along North Almora Thrust and South Almora Thrust, where reverse thrusting might have resulted in lowering of pressure and temperature condition. The developed model can be utilized for geological survey’s involving metamorphic terrain in inaccessible areas.

Computationally designed ionic liquid based molecularly imprinted@ graphene oxide composite: Characterization and validation

4-Hydroxy benzoic acid (4HA), a metabolite of Paraben is an emerging contaminant (EC) commonly used as preservative in cosmetics, pharmaceuticals, personal cares products and food industries. Removing metabolite of ECs can be one of the strategies to control their harmful effect on the ecosystem. Molecularly imprinted polymers synthesized using ionic liquids (ILMIPs) @ graphene oxide (GO) composites can be effective in this regard. The present work reports Density Functional Theory (DFT) to design the said composites by selecting suitable ionic liquid (IL) as functional monomer in order to get a stable template-monomer complex (TMC) with 4HA (template), an important parameter for creating imprinted sites for higher efficiency of the said composites. Simulated library of 17 ILs were created and their TMCs with 4HA were optimized. Amongst the17 ILs, TMCs of 1-allyl-3-octylimidazolium chloride (1A3OIC) exhibited highest and 1-vinyl-3-carboxy pentylimidazolium bromide(1V3CPB) depicted intermediate change in the Gibbs free energy values with 4HA. Simulated and experimental IR spectra of 4HA, selected ILs and their TMCs were compared for the identification of the functional groups involved in the formation of TMCs through hydrogen bonding. Theoretical studies of TMCs with GO moiety helped in designing efficient composite. Insight into the chemistry was obtained by Mulliken charge analysis. Experimental studies of synthesized ILMIPs after characterization confirmed conciliation with simulated data.

Data-driven fault detection and diagnosis for packaged rooftop units using statistical machine learning classification methods

This paper proposes and demonstrates a data-driven fault detection and diagnosis strategy for packaged rooftop units using statistical machine learning classification methods. The fault detection and diagnosis task is formulated as a multi-class classification problem. Seven typical rooftop unit faults are discriminated against one another as well as the normal condition. Since experimental data for rooftop units is rare and difficult to obtain, a simulated data library of model faults at steady state operation is used for training and validating the classifications models. Synthetic minority over-sampling technique is used to generate new artificial samples of minority class in order to balance the dataset. Nine well-known classification methods are applied to our dataset, and their performance is compared. The results show that support vector machine, with an overall accuracy rate of 96.2%, is the best classifier, and linear discriminant analysis, with an overall accuracy rate of 76.2%, is the worst classifier. The performance of the classification methods for individual faults is also characterized using true positive rate and false positive rate statistical measures. The relative importance of input variables is also discussed. The high accuracy of the classification methods shows the potential of a data-driven strategy in detecting and diagnosing the rooftop unit faults.

Novel digital image analysis using fractal dimension for assessment of skin radiance.

Despite a strong desire to quantify skin radiance in the field of cosmetics, there does not exist a robust method to characterize it. Classical shine that quantifies the specular reflection from skin has been commonly used as the metric to characterize radiance. However, it does not always correlate with the perceived radiance as there are many other parameters that inform radiance perception including spatial distribution of shine and color homogeneity. In this work, we propose a novel method using fractal analysis to better characterize radiance by considering the spatial heterogeneity of pixel intensities as well as color evenness. A simulated image library (nine images) from very dull to very bright was created using bare face images of 20 panelists. Product images taken post-product usage were ranked along this library by finding the image in the library that most resembles the product image by our algorithm as well as experts. Additionally, classical shine and color measurements were made as benchmarks. Our results confirm a strong correlation (R2 =0.99) between the expert radiance rankings and the rankings by fractal dimension algorithm. The new algorithm offers an improved product differentiation compared with classical shine or color measurements. Fractal dimension calculation offers higher sensitivity and resolution compared with other descriptors such as classical shine or color heterogeneity. In cases where the image rank is dominated by pixel intensities rather than color evenness, the image ranks resulting from calculating the fractal dimension is comparable with use of classical shine as the ranking parameter.

CycLS: Accurate, whole-library sequencing of cyclic peptides using tandem mass spectrometry

Cyclic peptides are of great interest as therapeutic compounds due to their potential for specificity and intracellular activity, but specific compounds can be difficult to identify from large libraries without resorting to molecular encoding techniques. Large libraries of cyclic peptides are often DNA-encoded or linearized before sequencing, but both of those deconvolution strategies constrain the chemistry, assays, and quantification methods which can be used. We developed an automated sequencing program, CycLS, to identify cyclic peptides contained within large synthetic libraries. CycLS facilitates quick and easy identification of all library-members via tandem mass spectrometry data without requiring any specific chemical moieties or modifications within the library. Validation of CycLS against a library of 400 cyclic hexapeptide peptoid hybrids (peptomers) of unique mass yielded a result of 95% accuracy when compared against a simulated library size of 234,256 compounds. CycLS was also evaluated by resynthesizing pure compounds from a separate 1800-member library of cyclic hexapeptides and hexapeptomers with high mass redundancy. Of 22 peptides resynthesized, 17 recapitulated the retention times and fragmentation patterns assigned to them from the whole-library bulk assay results. Implementing a database-matching approach, CycLS is fast and provides a robust method for sequencing cyclic peptides that is particularly applicable to the deconvolution of synthetic libraries.

Reassessment of the temperature-emissivity separation from multispectral thermal infrared data: Introducing the impact of vegetation canopy by simulating the cavity effect with the SAIL-Thermique model

We investigated the use of multispectral thermal imagery to retrieve land surface emissivity and temperature. Conversely to concurrent methods, the temperature emissivity separation (TES) method simply requires single overpass without any ancillary information. This is possible since TES makes use of an empirical relationship that estimates the minimum emissivity ε-min from the emissivity spectral contrast captured over several channels, so-called maximum-minimum difference (MMD). In previous studies, the ε-min - MMD empirical relationship of TES was calibrated and validated for various sensor spectral configurations, where the proposed calibrations involved single or linearly mixed spectra of emissivity at the leaf or soil level. However, cavity effect should be taken into account at the vegetation canopy level, to avoid an underestimation of emissivity, especially for intermediate vegetation conditions between bare soil and full vegetation cover.The current study aimed to evaluate the performances of the TES method when applied to vegetation canopies with cavity effect. We used the SAIL-Thermique model to simulate a library of emissivity spectra for a wide range of soil and plant conditions, and we addressed the spectral configurations of recent and forthcoming sensors. We obtained good results for calibration and validation over the simulated library, except for full cover canopies because of the TES gray body problem. Consistent with previous studies, the calibration/validation results were better with more channels that capture emissivity spectral contrast more efficiently. Our TES calibrations provided larger ε-min values as compared to former studies, especially for intermediate vegetation cover. We explained this trend by the simulated spectral library that involved numerous vegetation canopies with cavity effect, thereby shifting up the ε-min - MMD empirical relationship. Consequently, our TES calibration provided larger (respectively lower) estimates of emissivity (respectively radiometric temperature) that were likely to be more realistic as compared to previous calibrations. Finally, SAIL-Thermique simulations permitted to show that increasing Leaf Area Index induced a displacement of the (ε-min, MMD) pairs along the empirical relationship. This was consistent with the TES underlying assumption, where any change in ε-min induces changes in MMD since ε-max is bounded on [0.98–1]. Further investigations should focus on validating the outcomes of the current study against ground-based measurements, and on assessing TES performances when accounting for instrumental and atmospheric perturbations.

Multi-Algorithm Indices and Look-Up Table for Chlorophyll-a Retrieval in Highly Turbid Water Bodies Using Multispectral Data

Many approaches have been proposed for monitoring the eutrophication of Case 2 waters using remote sensing data. Semi-analytical algorithms and spectrum matching are two major approaches for chlorophyll-a (Chla) retrieval. Semi-analytical algorithms provide indices correlated with phytoplankton characteristics, (e.g., maximum and minimum absorption peaks). Algorithms’ indices are correlated with measured Chla through the regression process. The main drawback of the semi-analytical algorithms is that the derived relation is location and data limited. Spectrum matching and the look-up table approach rely on matching the measured reflectance with a large library of simulated references corresponding to wide ranges of water properties. The spectral matching approach taking hyperspectral measured reflectance as an input, leading to difficulties in incorporating data from multispectral satellites. Consequently, multi-algorithm indices and the look-up table (MAIN-LUT) technique is proposed to combine the merits of semi-analytical algorithms and look-up table, which can be applied to multispectral data. Eight combinations of four algorithms (i.e., 2-band, 3-band, maximum chlorophyll index, and normalized difference chlorophyll index) are investigated for the MAIN-LUT technique. In situ measurements and Medium Resolution Imaging Spectrometer (MERIS) sensor data are used to validate MAIN-LUT. In general, the MAIN-LUT provide a comparable retrieval accuracy with locally tuned algorithms. The most accurate of the locally tuned algorithms varied among datasets, revealing the limitation of these algorithms to be applied universally. In contrast, the MAIN-LUT provided relatively high retrieval accuracy for Tokyo Bay (R2 = 0.692, root mean square error (RMSE) = 21.4 mg m−3), Lake Kasumigaura (R2 = 0.866, RMSE = 11.3 mg m−3), and MERIS data over Lake Kasumigaura (R2 = 0.57, RMSE = 36.5 mg m−3). The simulated reflectance library of MAIN-LUT was generated based on inherent optical properties of Tokyo Bay; however, the MAIN-LUT also provided high retrieval accuracy for Lake Kasumigaura. MAIN-LUT could capture the spatial and temporal distribution of Chla concentration for Lake Kasumigaura.

Epsilon-Q: An Automated Analyzer Interface for Mass Spectral Library Search and Label-Free Protein Quantification.

Mass spectrometry (MS) is a widely used proteome analysis tool for biomedical science. In an MS-based bottom-up proteomic approach to protein identification, sequence database (DB) searching has been routinely used because of its simplicity and convenience. However, searching a sequence DB with multiple variable modification options can increase processing time, false-positive errors in large and complicated MS data sets. Spectral library searching is an alternative solution, avoiding the limitations of sequence DB searching and allowing the detection of more peptides with high sensitivity. Unfortunately, this technique has less proteome coverage, resulting in limitations in the detection of novel and whole peptide sequences in biological samples. To solve these problems, we previously developed the "Combo-Spec Search" method, which uses manually multiple references and simulated spectral library searching to analyze whole proteomes in a biological sample. In this study, we have developed a new analytical interface tool called "Epsilon-Q" to enhance the functions of both the Combo-Spec Search method and label-free protein quantification. Epsilon-Q performs automatically multiple spectral library searching, class-specific false-discovery rate control, and result integration. It has a user-friendly graphical interface and demonstrates good performance in identifying and quantifying proteins by supporting standard MS data formats and spectrum-to-spectrum matching powered by SpectraST. Furthermore, when the Epsilon-Q interface is combined with the Combo-Spec search method, called the Epsilon-Q system, it shows a synergistic function by outperforming other sequence DB search engines for identifying and quantifying low-abundance proteins in biological samples. The Epsilon-Q system can be a versatile tool for comparative proteome analysis based on multiple spectral libraries and label-free quantification.

Combination of Multiple Spectral Libraries Improves the Current Search Methods Used to Identify Missing Proteins in the Chromosome-Centric Human Proteome Project.

Approximately 2.9 billion long base-pair human reference genome sequences are known to encode some 20 000 representative proteins. However, 3000 proteins, that is, ~15% of all proteins, have no or very weak proteomic evidence and are still missing. Missing proteins may be present in rare samples in very low abundance or be only temporarily expressed, causing problems in their detection and protein profiling. In particular, some technical limitations cause missing proteins to remain unassigned. For example, current mass spectrometry techniques have high limits and error rates for the detection of complex biological samples. An insufficient proteome coverage in a reference sequence database and spectral library also raises major issues. Thus, the development of a better strategy that results in greater sensitivity and accuracy in the search for missing proteins is necessary. To this end, we used a new strategy, which combines a reference spectral library search and a simulated spectral library search, to identify missing proteins. We built the human iRefSPL, which contains the original human reference spectral library and additional peptide sequence-spectrum match entries from other species. We also constructed the human simSPL, which contains the simulated spectra of 173 907 human tryptic peptides determined by MassAnalyzer (version 2.3.1). To prove the enhanced analytical performance of the combination of the human iRefSPL and simSPL methods for the identification of missing proteins, we attempted to reanalyze the placental tissue data set (PXD000754). The data from each experiment were analyzed using PeptideProphet, and the results were combined using iProphet. For the quality control, we applied the class-specific false-discovery rate filtering method. All of the results were filtered at a false-discovery rate of <1% at the peptide and protein levels. The quality-controlled results were then cross-checked with the neXtProt DB (2014-09-19 release). The two spectral libraries, iRefSPL and simSPL, were designed to ensure no overlap of the proteome coverage. They were shown to be complementary to spectral library searching and significantly increased the number of matches. From this trial, 12 new missing proteins were identified that passed the following criterion: at least 2 peptides of 7 or more amino acids in length or one of 9 or more amino acids in length with one or more unique sequences. Thus, the iRefSPL and simSPL combination can be used to help identify peptides that have not been detected by conventional sequence database searches with improved sensitivity and a low error rate.

Confident site localization using a simulated phosphopeptide spectral library.

We have investigated if phosphopeptide identification and simultaneous site localization can be achieved by spectral library searching. This allows taking advantage of comparison of specific spectral features, which would lead to improved discrimination of differential localizations. For building a library, we propose a spectral simulation strategy where all possible single phosphorylations can be simply and accurately (re)constructed on enzymatically dephosphorylated peptides, by predicting the diagnostic fragmentation events produced in beam-type CID. To demonstrate the performance of our approach, enriched HeLa phosphopeptides were dephosphorylated with alkaline phosphatase and analyzed with higher energy collisional dissociation (HCD), which were then used for creating a spectral library of simulated phosphopeptides. Spectral library searching using SpectraST was performed on data sets of synthetic phosphopeptides and the HeLa phosphopeptides, and subsequently compared to Mascot and Sequest database searching followed by phosphoRS and Ascore afforded localization, respectively. Our approach successfully led to accurate localization, and it outperformed other methods, when phosphopeptides were covered by the library. These results suggest that the searching with simulated spectral libraries serves as a crucial approach for both supplementing and validating the phosphorylation sites obtained by database searching and localization tools. For future development, simulation of multiply phosphorylated peptides remains to be implemented.

SimFuse: A Novel Fusion Simulator for RNA Sequencing (RNA-Seq) Data.

The performance evaluation of fusion detection algorithms from high-throughput sequencing data crucially relies on the availability of data with known positive and negative cases of gene rearrangements. The use of simulated data circumvents some shortcomings of real data by generation of an unlimited number of true and false positive events, and the consequent robust estimation of accuracy measures, such as precision and recall. Although a few simulated fusion datasets from RNA Sequencing (RNA-Seq) are available, they are of limited sample size. This makes it difficult to systematically evaluate the performance of RNA-Seq based fusion-detection algorithms. Here, we present SimFuse to address this problem. SimFuse utilizes real sequencing data as the fusions' background to closely approximate the distribution of reads from a real sequencing library and uses a reference genome as the template from which to simulate fusions' supporting reads. To assess the supporting read-specific performance, SimFuse generates multiple datasets with various numbers of fusion supporting reads. Compared to an extant simulated dataset, SimFuse gives users control over the supporting read features and the sample size of the simulated library, based on which the performance metrics needed for the validation and comparison of alternative fusion-detection algorithms can be rigorously estimated.

Investigation of Scrambled Ions in Tandem Mass Spectra, Part 2. On the Influence of the Ions on Peptide Identification

A comprehensive investigation was performed to understand the influence of sequence scrambling in peptide ions on peptide identification results. To achieve this, four tandem mass spectrometry datasets with scrambled ions included and with them excluded were analyzed by Crux, X!Tandem, SpectraST, Lutefisk, and PepNovo. While the different algorithms differed in their performance, an increase in the number of correctly identified peptides was generally observed when removing scrambled ions, with the exception of the SpectraST algorithm. However, the variation of the match scores upon removal was unpredictable. Following these investigations, an interpretation was given on how the scrambled ions affect peptide identification. Lastly, a simulated theoretical mass spectral library derived from the NIST peptide Libraries was constructed and searched by SpectraST to study whether scrambled ions in predicted mass spectra could affect peptide identification. Consistent with the peptide library search results, no significant variations for dot product scores as well as peptide identification results were observed when these ions were included in the theoretical MS/MS spectra. From the five adopted algorithms, the SpectraST and Crux provided the most robust results, whereas X!Tandem, PepNovo, and Lutefisk were sensitive to the existence of the scrambled ions, especially the latter two de novo sequencing algorithms.

Spectrum-to-Spectrum Searching Using a Proteome-wide Spectral Library

The unambiguous assignment of tandem mass spectra (MS/MS) to peptide sequences remains a key unsolved problem in proteomics. Spectral library search strategies have emerged as a promising alternative for peptide identification, in which MS/MS spectra are directly compared against a reference library of confidently assigned spectra. Two problems relate to library size. First, reference spectral libraries are limited to rediscovery of previously identified peptides and are not applicable to new peptides, because of their incomplete coverage of the human proteome. Second, problems arise when searching a spectral library the size of the entire human proteome. We observed that traditional dot product scoring methods do not scale well with spectral library size, showing reduction in sensitivity when library size is increased. We show that this problem can be addressed by optimizing scoring metrics for spectrum-to-spectrum searches with large spectral libraries. MS/MS spectra for the 1.3 million predicted tryptic peptides in the human proteome are simulated using a kinetic fragmentation model (MassAnalyzer version2.1) to create a proteome-wide simulated spectral library. Searches of the simulated library increase MS/MS assignments by 24% compared with Mascot, when using probabilistic and rank based scoring methods. The proteome-wide coverage of the simulated library leads to 11% increase in unique peptide assignments, compared with parallel searches of a reference spectral library. Further improvement is attained when reference spectra and simulated spectra are combined into a hybrid spectral library, yielding 52% increased MS/MS assignments compared with Mascot searches. Our study demonstrates the advantages of using probabilistic and rank based scores to improve performance of spectrum-to-spectrum search strategies.

A Simulated MS/MS Library for Spectrum-to-spectrum Searching in Large Scale Identification of Proteins

Identifying peptides from mass spectrometric fragmentation data (MS/MS spectra) using search strategies that map protein sequences to spectra is computationally expensive. An alternative strategy uses direct spectrum-to-spectrum matching against a reference library of previously observed MS/MS that has the advantage of evaluating matches using fragment ion intensities and other ion types than the simple set normally used. However, this approach is limited by the small sizes of the available peptide MS/MS libraries and the inability to evaluate the rate of false assignments. In this study, we observed good performance of simulated spectra generated by the kinetic model implemented in MassAnalyzer (Zhang, Z. (2004) Prediction of low-energy collision-induced dissociation spectra of peptides. Anal. Chem. 76, 3908-3922; Zhang, Z. (2005) Prediction of low-energy collision-induced dissociation spectra of peptides with three or more charges. Anal. Chem. 77, 6364-6373) as a substitute for the reference libraries used by the spectrum-to-spectrum search programs X!Hunter and BiblioSpec and similar results in comparison with the spectrum-to-sequence program Mascot. We also demonstrate the use of simulated spectra for searching against decoy sequences to estimate false discovery rates. Although we found lower score discrimination with spectrum-to-spectrum searches than with Mascot, particularly for higher charge forms, comparable peptide assignments with low false discovery rate were achieved by examining consensus between X!Hunter and Mascot, filtering results by mass accuracy, and ignoring score thresholds. Protein identification results are comparable to those achieved when evaluating consensus between Sequest and Mascot. Run times with large scale data sets using X!Hunter with the simulated spectral library are 7 times faster than Mascot and 80 times faster than Sequest with the human International Protein Index (IPI) database. We conclude that simulated spectral libraries greatly expand the search space available for spectrum-to-spectrum searching while enabling principled analyses and that the approach can be used in consensus strategies for large scale studies while reducing search times.

Mapping coral reef benthic substrates using hyperspectral space-borne images and spectral libraries

The suitability of Hyperion, the first civilian hyperspectral sensor in space, for mapping coral reef benthic substrates has been investigated in this study. An image of Cairns Reef, in the northern section of the Australian Great Barrier Reef (GBR), was acquired during Hyperion Calibration and Validation activities. A field experiment was carried out on Cairns Reef to collect information about the optical properties of the water in the area and to map benthic cover by means of video transects. An approach was used to classify the Hyperion image that allows convenient mapping of benthic substrate type and water depth simultaneously. A hyperspectral library of radiance at Hyperion altitude was simulated using a spectral library of GBR benthic substrates, a Hydrolight 4.1 radiative transfer model, and an in-house atmospheric model similar to Modtran-3.7. The image was then classified using the Hyperion at-sensor radiance data and the Spectral Angle Mapper metric using the simulated at-sensor spectral library. The results suggest that using spectral libraries created with forward modelling from the sea bottom to top of the atmosphere are useful tools for interpretation of reefs and can give better results in image classification than classifying the image after removing atmospheric and water column effects. The results also suggest that bottom type and water depth can be separated and mapped simultaneously provided hyperspectral data is available.

Using the Monte Carlo – Library Least-Squares (MCLLS) approach for the in vivo XRF measurement of lead in bone

The Monte Carlo – Library Least-Squares (MCLLS) method has been developed by the Center for Engineering Applications of Radioisotopes for various XRF applications of multi-elemental composition analysis and implemented with the CEARXRF code. In the present work, it is successfully applied to the in vivo XRF measurement of lead in bone and benchmarked by the measurement of a plaster of Paris phantom of known lead concentration. It is implicitly assumed that if the approach works for this sample that closely approximates the real problem of interest, it will also work for the real in vivo case when the proper description of the real case is used. Traditional techniques for XRF analysis are reviewed briefly and the full advantages of the MCLLS method are discussed. Simulation results are presented that are in good agreement with experimental results. The applicability of the MCLLS method to the lead in bone measurement is supported by the good fitting results obtained with simulated Monte Carlo elemental library spectra and close agreement between simulated and experimental spectra from a calcium-rich matrix-based calibration standard in a test geometrical configuration.

Fluorescence-activated cell sorting of specific affibody-displaying staphylococci.

Efficient enrichment of staphylococcal cells displaying specific heterologous affinity ligands on their cell surfaces was demonstrated by using fluorescence-activated cell sorting. Using bacterial surface display of peptide or protein libraries for the purpose of combinatorial protein engineering has previously been investigated by using gram-negative bacteria. Here, the potential for using a gram-positive bacterium was evaluated by employing the well-established surface expression system for Staphylococcus carnosus. Staphylococcus aureus protein A domains with binding specificity to immunoglobulin G or engineered specificity for the G protein of human respiratory syncytial virus were expressed as surface display on S. carnosus cells. The surface accessibility and retained binding specificity of expressed proteins were demonstrated in whole-cell enzyme and flow cytometry assays. Also, affibody-expressing target cells could be sorted essentially quantitatively from a moderate excess of background cells in a single step by using a high-stringency sorting mode. Furthermore, in a simulated library selection experiment, a more-than-25,000-fold enrichment of target cells could be achieved through only two rounds of cell sorting and regrowth. The results obtained indicate that staphylococcal surface display of affibody libraries combined with fluoresence-activated cell sorting might indeed constitute an attractive alternative to existing technology platforms for affinity-based selections.